New Developments in Unsupervised Outlier Detection - Xiaochun Wang, Xiali Wang, Mitch Wilkes

New Developments in Unsupervised Outlier Detection (eBook)

Algorithms and Applications

, , (Autoren)

eBook Download: PDF
2020 | 1st ed. 2021
XXI, 277 Seiten
Springer Singapore (Verlag)
978-981-15-9519-6 (ISBN)
Systemvoraussetzungen
171,19 € inkl. MwSt
  • Download sofort lieferbar
  • Zahlungsarten anzeigen
This book enriches unsupervised outlier detection research by proposing several new distance-based and density-based outlier scores in a k-nearest neighbors' setting. The respective chapters highlight the latest developments in k-nearest neighbor-based outlier detection research and cover such topics as our present understanding of unsupervised outlier detection in general; distance-based and density-based outlier detection in particular; and the applications of the latest findings to boundary point detection and novel object detection. The book also offers a new perspective on bridging the gap between k-nearest neighbor-based outlier detection and clustering-based outlier detection, laying the groundwork for future advances in unsupervised outlier detection research.

The authors hope the algorithms and applications proposed here will serve as valuable resources for outlier detection researchers for years to come.



Xiaochun Wang received her B.S. degree from Beijing University and the Ph.D. degree from the Department of Electrical Engineering and Computer Science, Vanderbilt University, the United States of America. She is currently an Associate Professor of the School of Software Engineering at Xi'an Jiaotong University. Her research interests are in computer vision, signal processing, data mining, machine learning and pattern recognition.

Xia Li Wang received his Ph.D. degree from the Department of Computer Science, Northwest University, People's Republic of China, in 2005. He is a faculty member in the School of Information Engineering, Chang'an University, China. His research interests are in computer vision, signal processing, intelligent traffic system, and pattern recognition.

D. Mitchell Wilkes received the B.S.E.E. degree from Florida Atlantic, and the M.S.E.E. and Ph.D. degrees from Georgia Institute of Technology. His research interests include digital signal processing, image processing and computer vision, structurally adaptive systems, sonar, as well as signal modeling. He is a member of the IEEE and a faculty member at the Department of Electrical Engineering and Computer Science, Vanderbilt University. He is a member of the IEEE.        

This book enriches unsupervised outlier detection research by proposing several new distance-based and density-based outlier scores in a k-nearest neighbors' setting. The respective chapters highlight the latest developments in k-nearest neighbor-based outlier detection research and cover such topics as our present understanding of unsupervised outlier detection in general; distance-based and density-based outlier detection in particular; and the applications of the latest findings to boundary point detection and novel object detection. The book also offers a new perspective on bridging the gap between k-nearest neighbor-based outlier detection and clustering-based outlier detection, laying the groundwork for future advances in unsupervised outlier detection research.The authors hope the algorithms and applications proposed here will serve as valuable resources for outlier detection researchers for years to come.
Erscheint lt. Verlag 24.11.2020
Zusatzinfo XXI, 277 p. 138 illus., 120 illus. in color.
Sprache englisch
Themenwelt Informatik ► Datenbanken ► Data Warehouse / Data Mining
Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Technik
Schlagworte Boundary Point Detection • Clustering Based Outlier Detection • Density-Based Outlier Detection • Distance-Based Outlier Detection • k-Nearest Neighbors Based Outlier Detection • Novel Object Detection • Unsupervised Outlier Detection
ISBN-10 981-15-9519-4 / 9811595194
ISBN-13 978-981-15-9519-6 / 9789811595196
Haben Sie eine Frage zum Produkt?
PDFPDF (Wasserzeichen)
Größe: 9,9 MB

DRM: Digitales Wasserzeichen
Dieses eBook enthält ein digitales Wasser­zeichen und ist damit für Sie persona­lisiert. Bei einer missbräuch­lichen Weiter­gabe des eBooks an Dritte ist eine Rück­ver­folgung an die Quelle möglich.

Dateiformat: PDF (Portable Document Format)
Mit einem festen Seiten­layout eignet sich die PDF besonders für Fach­bücher mit Spalten, Tabellen und Abbild­ungen. Eine PDF kann auf fast allen Geräten ange­zeigt werden, ist aber für kleine Displays (Smart­phone, eReader) nur einge­schränkt geeignet.

Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen dafür einen PDF-Viewer - z.B. den Adobe Reader oder Adobe Digital Editions.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen dafür einen PDF-Viewer - z.B. die kostenlose Adobe Digital Editions-App.

Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.

Print-Ausgabe
Buch | Hardcover
181,89 €
Mehr entdecken
aus dem Bereich
Data Privacy in der Praxis - Katharine Jarmul
Data Privacy in der Praxis
Datenschutz und Sicherheit in Daten- und KI-Projekten

von Katharine Jarmul

eBook Download (2024)
O'Reilly Verlag
24,99 €
auf Facebook teilen
bei Twitter
Link zu dieser Seite kopieren